Investigations into Effect of Neural Network Predictive Control of UPFC for Improving Transient Stability Performance of Multimachine Power System
The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller.
[1] P. Kundur, J. Paserba, V. Ajjarapu, G. Andersson, A. Bose, C. Canizares, N. Hatziargyriou, D. Hill, A. Stankovic, C. Taylor, T. V. Cutsem, and V. Vittal, "Definition and classification of power system stability,” IEEE Trans. Power Systems, vol. 19, no. 2, pp. 1387-1401, 2004.
[2] N. G. Hingorani, and L. Gyuvgi, Understanding FACTS concepts & technology of flexible AC transmission systems. Delhi: IEEE Press, 2001, ch. 1.
[3] M. Vilathgamuwa, X. Zhu, and S. S. Choi, "A robust control method to improve the performance of a unified power flow controller,” Electrical Power System Research, vol. 55, pp. 103-111, 2000.
[4] B. C. Pal, "Robust damping of interarea oscillations with unified power flow controller,” IEE Proceedings on Generation, Transmission, Distribution, vol. 149, no. 6, pp. 733-738, 2002.
[5] A. H. M. A. Rahim, and S. A. Al-Baiyat, "A robust damping controller design for a unified power flow controller,” in Proc. 39th IEEE Universities Power Engineering Conference, (UPEC), IEEE, Bristol, 2004, pp. 265-269.
[6] A.H M.A. Rahim, J. M. Bakhashwain, and S. A. Al-Baiyat, "Robust damping controls for a unified power flow controller,” International Journal of Emerging Electric Power Systems, vol. 6, no. 2, pp. 1-21, 2006.
[7] S. A. Taher, S. Akbari, A. Abdolalipour, and R. Hematti, "Robust decentralized controller design for UPFC using µ-synthesis,” Communications in Nonlinear Science and Numerical Simulation, vol. 15, pp. 2149-2161, 2010.
[8] A.T. Al-Awami, Y. L. Abdel-Magid, and M. A. Abido, "Simultaneous stabilization of power system using UPFC-based controllers,” Electric Power Components and Systems, vol. 34, no. 9, pp. 941-959, 2006.
[9] K. Wang, B. Yan, M. L. Crow, and D. Gan, "A feedback linearization baed unified power flow controller internal controller for power flow control,” Electric Power Components and Systems, vol. 40, no. 6, pp. 628-647, 2012.
[10] V. Azbe, U. Gabrijel, D. Povh, and R. Mihalic, "The energy function of a general multimachine system with a unified power flow controller,” IEEE Trans. Power Syst. vol. 20, no. 3, pp. 1478-1485, 2005.
[11] A. Zangeneh, A. Kazemi, M. Hajatipour, and S. Jadid, "A Lyapunov theory based UPFC controller for power flow control,” International Journal of Electric Power and Energy Systems, vol. 31, pp. 302-308, 2009.
[12] Sheela Tiwari, Ram Naresh, R. Jha, "Neural network predictive control of UPFC for improving transient stability performance of power system,” Applied Soft Computing, vol.11, no. 8, pp.4581-4590, 2011.
[13] Sheela Tiwari, Ram Naresh, R. Jha, "Neural network predictive control of UPFC for enhancing transient stability performance of a single machine infinite bus system,” International Journal of Advancements in Electronics and Electrical Engineering, vol. 1, no. 2, pp.100-104, 2012.
[14] M. Klein, G. J. Rogers, and P. Kundur, "A fundamental study of inter-area oscillations in power systems,” IEEE Transactions on Power Systems, vol. 6, no. 3, pp.914-921, 1991.
[15] S. Mishra, "Neural network based adaptive UPFC for improving transient stability performance of power system,” IEEE Transactions on Neural Networks, vol. 17, no. 2, pp. 461-470, 2006.
[16] D. W. Clarke, C. Mohtadi, and P. C. Tuffs, "Generalized Predictive Control – Part I: The Basic Algorithm,” Automatica, vol. 23, no. 2, pp. 137-148, 1987.
[17] D. W. Clarke, C. Mohtadi, and P. C. Tuffs, "Generalized Predictive Control – Part II: Extensions and Interpretations,” Automatica, vol. 23, no. 2, pp. 149-160, 1987.
[18] D. W. Clarke, and C. Mohtadi, "Properties of Generalized predictive Control,” Automatica, vol. 25, no. 6, pp. 859-875, 1989.
[19] D. Soloway, and P.J. Haley, "Neural generalized predictive control- A Newton-Raphson implementation,” in: Proc. IEEE International Symposium on Intelligent Control, Michigan, USA, September 15-18, 1996, pp. 277-282.
[20] E. F. Camacho, and C. Bordons, Model Predictive Control. London: Springer-Veralag GmbH, 2004.
[21] J. E. Dennis, and R. B. Schnabel, Numerical methods for unconstrained optimization and nonlinear equations. Philadelphia: SIAM, 1996.
[1] P. Kundur, J. Paserba, V. Ajjarapu, G. Andersson, A. Bose, C. Canizares, N. Hatziargyriou, D. Hill, A. Stankovic, C. Taylor, T. V. Cutsem, and V. Vittal, "Definition and classification of power system stability,” IEEE Trans. Power Systems, vol. 19, no. 2, pp. 1387-1401, 2004.
[2] N. G. Hingorani, and L. Gyuvgi, Understanding FACTS concepts & technology of flexible AC transmission systems. Delhi: IEEE Press, 2001, ch. 1.
[3] M. Vilathgamuwa, X. Zhu, and S. S. Choi, "A robust control method to improve the performance of a unified power flow controller,” Electrical Power System Research, vol. 55, pp. 103-111, 2000.
[4] B. C. Pal, "Robust damping of interarea oscillations with unified power flow controller,” IEE Proceedings on Generation, Transmission, Distribution, vol. 149, no. 6, pp. 733-738, 2002.
[5] A. H. M. A. Rahim, and S. A. Al-Baiyat, "A robust damping controller design for a unified power flow controller,” in Proc. 39th IEEE Universities Power Engineering Conference, (UPEC), IEEE, Bristol, 2004, pp. 265-269.
[6] A.H M.A. Rahim, J. M. Bakhashwain, and S. A. Al-Baiyat, "Robust damping controls for a unified power flow controller,” International Journal of Emerging Electric Power Systems, vol. 6, no. 2, pp. 1-21, 2006.
[7] S. A. Taher, S. Akbari, A. Abdolalipour, and R. Hematti, "Robust decentralized controller design for UPFC using µ-synthesis,” Communications in Nonlinear Science and Numerical Simulation, vol. 15, pp. 2149-2161, 2010.
[8] A.T. Al-Awami, Y. L. Abdel-Magid, and M. A. Abido, "Simultaneous stabilization of power system using UPFC-based controllers,” Electric Power Components and Systems, vol. 34, no. 9, pp. 941-959, 2006.
[9] K. Wang, B. Yan, M. L. Crow, and D. Gan, "A feedback linearization baed unified power flow controller internal controller for power flow control,” Electric Power Components and Systems, vol. 40, no. 6, pp. 628-647, 2012.
[10] V. Azbe, U. Gabrijel, D. Povh, and R. Mihalic, "The energy function of a general multimachine system with a unified power flow controller,” IEEE Trans. Power Syst. vol. 20, no. 3, pp. 1478-1485, 2005.
[11] A. Zangeneh, A. Kazemi, M. Hajatipour, and S. Jadid, "A Lyapunov theory based UPFC controller for power flow control,” International Journal of Electric Power and Energy Systems, vol. 31, pp. 302-308, 2009.
[12] Sheela Tiwari, Ram Naresh, R. Jha, "Neural network predictive control of UPFC for improving transient stability performance of power system,” Applied Soft Computing, vol.11, no. 8, pp.4581-4590, 2011.
[13] Sheela Tiwari, Ram Naresh, R. Jha, "Neural network predictive control of UPFC for enhancing transient stability performance of a single machine infinite bus system,” International Journal of Advancements in Electronics and Electrical Engineering, vol. 1, no. 2, pp.100-104, 2012.
[14] M. Klein, G. J. Rogers, and P. Kundur, "A fundamental study of inter-area oscillations in power systems,” IEEE Transactions on Power Systems, vol. 6, no. 3, pp.914-921, 1991.
[15] S. Mishra, "Neural network based adaptive UPFC for improving transient stability performance of power system,” IEEE Transactions on Neural Networks, vol. 17, no. 2, pp. 461-470, 2006.
[16] D. W. Clarke, C. Mohtadi, and P. C. Tuffs, "Generalized Predictive Control – Part I: The Basic Algorithm,” Automatica, vol. 23, no. 2, pp. 137-148, 1987.
[17] D. W. Clarke, C. Mohtadi, and P. C. Tuffs, "Generalized Predictive Control – Part II: Extensions and Interpretations,” Automatica, vol. 23, no. 2, pp. 149-160, 1987.
[18] D. W. Clarke, and C. Mohtadi, "Properties of Generalized predictive Control,” Automatica, vol. 25, no. 6, pp. 859-875, 1989.
[19] D. Soloway, and P.J. Haley, "Neural generalized predictive control- A Newton-Raphson implementation,” in: Proc. IEEE International Symposium on Intelligent Control, Michigan, USA, September 15-18, 1996, pp. 277-282.
[20] E. F. Camacho, and C. Bordons, Model Predictive Control. London: Springer-Veralag GmbH, 2004.
[21] J. E. Dennis, and R. B. Schnabel, Numerical methods for unconstrained optimization and nonlinear equations. Philadelphia: SIAM, 1996.
@article{"International Journal of Electrical, Electronic and Communication Sciences:57739", author = "Sheela Tiwari and R. Naresh and R. Jha", title = "Investigations into Effect of Neural Network Predictive Control of UPFC for Improving Transient Stability Performance of Multimachine Power System", abstract = "The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller.
", keywords = "Identification, Neural networks, Predictive control, Transient stability, UPFC. ", volume = "7", number = "12", pages = "1555-6", }
